Cervical cancer is the most common cancer affecting sub-Saharan African women and is prevalent among HIV-positive (HIV+) individuals. No comprehensive profiling of cancer genomes, transcriptomes or epigenomes has been performed in this population thus far. We characterized 118 tumors from Ugandan patients, of whom 72 were HIV+, and performed extended mutation analysis on an additional 89 tumors. We detected human papillomavirus (HPV)-clade-specific differences in tumor DNA methylation, promoter- and enhancer-associated histone marks, gene expression and pathway dysregulation. Changes in histone modification at HPV integration events were correlated with upregulation of nearby genes and endogenous retroviruses.
Resistance of castration‐recurrent prostate cancer (CRPC) to enzalutamide and abiraterone involves the expression of constitutively active, truncated androgen receptor (AR) splice variants (AR‐Vs) that lack a C‐terminal ligand‐binding domain (LBD). Both full‐length AR and truncated AR‐Vs require a functional N‐terminal domain (NTD) for transcriptional activity thereby providing rationale for development of ralaniten (EPI‐002) as a first‐in‐class antagonist of the AR‐NTD. Here we evaluated the antitumor effect of a next‐generation analog of ralaniten (EPI‐7170) as a monotherapy or in combination with enzalutamide in prostate cancer cells that express AR‐V7 that were resistant to enzalutamide. EPI‐7170 had 8‐9 times improved potency compared to ralaniten. Enzalutamide increased levels of AR‐V7 and expression of its target genes. Knockdown of AR‐V7 restored sensitivity to enzalutamide, indicating a role for AR‐V7 in the mechanism of resistance. EPI‐7170 inhibited expression of genes transcriptionally regulated by full‐length AR and AR‐V7. A combination of EPI‐7170 and enzalutamide resulted in synergistic inhibition of proliferation of enzalutamide resistant cells that was consistent with results from cell cycle and clonogenic assays. In addition, this drug enhanced the anti‐tumor effect of enzalutamide in enzalutamide‐resistant CRPC preclinical models. Thus, a combination therapy targeting both the NTD and LBD of AR, and thereby blocking both full‐length AR and AR‐Vs, has potential for the treatment of enzalutamide‐resistant CRPC.
Cell-free DNA (cfDNA) has become a comprehensive biomarker in the fields of non-invasive cancer detection and monitoring, organ transplantation, prenatal genetic testing and pathogen detection. While cfDNA samples can be obtained using a broad variety of approaches, there is an urgent need to standardize analytical tools aimed at assessing its basic properties. Typical methods to determine the yield and fragment size distribution of cfDNA samples are usually either blind to genomic DNA contamination or the presence of enzymatic inhibitors, which can confound and undermine downstream analyses. Here, we present a novel droplet digital PCR assay to identify suboptimal samples and aberrant cfDNA size distributions, the latter typically associated with high levels of circulating tumour DNA (ctDNA). Our assay was designed to promiscuously cross-amplify members of the human olfactory receptor (OR) gene family and includes a customizable diploid locus for the determination of absolute cfDNA concentrations. We demonstrate here the utility of our assay to estimate the yield and quality of cfDNA extracts and deduce fragment size distributions that correlate well with those inferred by capillary electrophoresis and high throughput sequencing. The assay described herein is a powerful tool to establish quality controls and stratify cfDNA samples based on presumed ctDNA levels, then facilitating the implementation of robust, cost-effective and standardized analytical workflows into clinical practice.
Blocking androgen receptor (AR) transcriptional activity by androgen deprivation therapy (ADT) improves the response to radiotherapy for intermediate and high risk prostate cancer. Unfortunately, ADT, antiandrogens, and abiraterone increase expression of constitutively active splice variants of AR (AR-Vs) which regulate DNA damage repair leading to resistance to radiotherapy. Here we investigate whether blocking the transcriptional activities of full-length AR and AR-Vs with ralaniten leads to enhanced sensitivity to radiotherapy. Combination therapies using ralaniten with ionizing radiation were evaluated for effects on proliferation, colony formation, cell cycle, DNA damage, and Western blot analyses in human prostate cancer cells that express both full-length AR and AR-Vs. Ralaniten and a potent next-generation analog (EPI-7170) decreased expression of DNA repair genes whereas enzalutamide had no effect. FACS analysis revealed a dose-dependent decrease of BrdU incorporation with increased accumulation of γH2AX with a combination of ionizing radiation with ralaniten. An additive inhibitory effect on proliferation of enzalutamide-resistant cells was achieved with a combination of ralaniten compounds with ionizing radiation. Ralaniten and EPI-7170 sensitized prostate cancer cells that express full-length AR and AR-Vs to radiotherapy whereas enzalutamide had no added benefit.
Motivation: Networks are used to relate topological structure to system dynamics and function, particularly in ecology and systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive plots, one of many network visualizations, distinguish themselves as providing a general, consistent, and coherent rule-based representation to motivate hypothesis development and testing.
Results: Here, we present HyPE, Hive Panel Explorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous rendering of multiple network views. We demonstrate HyPE's features by exploring a microbial co-occurrence network constructed from forest soil microbiomes.
Availability: HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. A wiki, including a tutorial, is available at https://github.com/hallamlab/HivePanelExplorer/wiki.
Supplementary information: Supplementary data are available at Bioinformatics online.
Background: Tattoos may cause a variety of adverse reactions in the body, including immune reactions and infections. However, it is unknown whether tattoos may increase the risk of lymphatic cancers such as non-Hodgkin Lymphoma (NHL) and multiple myeloma (MM).
Methods: Participants from two population-based case-control studies were including in logistic regression models to examine the association between tattoos and risk of NHL and MM.
Results: A total of 1518 participants from the NHL study (737 cases) and 742 participants from the MM study (373 cases) were included in the analyses. No statistically significant associations were found between tattoos and risk of NHL or MM after adjusting for age, sex, ethnicity, education, BMI, and family history.
Conclusions: We did not identify any significant associations between tattoos and risk of MM, NHL, or NHL subtypes in these studies.
Impact: Though biologically plausible, tattoos were not associated with increased risk of NHL or MM in this study. Future studies with greater detail regarding tattoo exposure may provide further insights.
Aim: To provide a comprehensive understanding of gene regulatory networks in the developing human brain and a foundation for interpreting pathogenic deregulation. Materials & methods: We generated reference epigenomes and transcriptomes of dissected brain regions and primary neural progenitor cells (NPCs) derived from cortical and ganglionic eminence tissues of four normal human fetuses. Results: Integration of these data across developmental stages revealed a directional increase in active regulatory states, transcription factor activities and gene transcription with developmental stage. Consistent with differences in their biology, NPCs derived from cortical and ganglionic eminence regions contained common, region specific, and gestational week specific regulatory states. Conclusion: We provide a high-resolution regulatory network for NPCs from different brain regions as a comprehensive reference for future studies.
Purpose: Previous studies indicate that breast cancer molecular subtypes differ with respect to their dependency on autophagy, but our knowledge of the differential expression and prognostic significance of autophagy-related biomarkers in breast cancer is limited.
Methods: Immunohistochemistry (IHC) was performed on tissue microarrays from a large population of 3992 breast cancer patients divided into training and validation cohorts. Consensus staining scores were used to evaluate the expression levels of autophagy proteins LC3B, ATG4B, and GABARAP and determine the associations with clinicopathological variables and molecular biomarkers. Survival analyses were performed using the Kaplan-Meier function and Cox proportional hazards regression models.
Results: We found subtype-specific expression differences for ATG4B, with its expression lowest in basal-like breast cancer and highest in Luminal A, but there were no significant associations with patient prognosis. LC3B and GABARAP levels were highest in basal-like breast cancers, and high levels were associated with worse outcomes across all subtypes (DSS; GABARAP: HR 1.43, LC3B puncta: HR 1.43). High ATG4B levels were associated with ER, PR, and BCL2 positivity, while high LC3B and GABARAP levels were associated with ER, PR, and BCL2 negativity, as well as EGFR, HER2, HER3, CA-IX, PD-L1 positivity, and high Ki67 index (p < 0.05 for all associations). Exploratory multi-marker analysis indicated that the combination of ATG4B and GABARAP with LC3B could be useful for further stratifying patient outcomes.
Conclusions: ATG4B levels varied across breast cancer subtypes but did not show prognostic significance. High LC3B expression and high GABARAP expression were both associated with poor prognosis and with clinicopathological characteristics of aggressive disease phenotypes in all breast cancer subtypes.
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/. This article is protected by copyright. All rights reserved.